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首页> 外文期刊>IEEE transactions on industrial informatics >FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications
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FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications

机译：FDA <内联惯例> $ ^ 3 $ ：联合防范对基于云的Ioiot应用程序的对抗攻击

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摘要

Along with the proliferation of artificial intelligence and Internet of things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool deep neural networks (DNNs) used by industrial IoT (IIoT) applications. Due to biased training data or vulnerable underlying models, imperceptible modifications on inputs made by adversarial attacks may result in devastating consequences. Although existing methods are promising in defending such malicious attacks, most of them can only deal with limited existing attack types, which makes the deployment of large-scale IIoT devices a great challenge. To address this problem, in this article, we present an effective federated defense approach named FDA3 that can aggregate defense knowledge against adversarial examples from different sources. Inspired by federated learning, our proposed cloud-based architecture enables the sharing of defense capabilities against different attacks among IIoT devices. Comprehensive experimental results show that the generated DNNs by our approach can not only resist more malicious attacks than existing attack-specific adversarial training methods, but also prevent IIoT applications from new attacks.
机译：随着人工智能和物联网（物联网）技术的扩散，各种对抗攻击越来越涌现出愚弄工业物联网（IIT）应用使用的深神经网络（DNN）。由于偏见的培训数据或易受攻击的潜在模型，对抗性攻击所产生的难以察觉的修改可能导致破坏性后果。虽然现有方法在捍卫这种恶意攻击方面很有希望，但大多数人只能处理有限的现有攻击类型，这使得大规模IIOT设备部署成为一个巨大的挑战。为了解决这个问题，在本文中，我们提出了一个有效的联邦防御方法，命名为FDA3，可以将防御知识汇总来自不同来源的对抗示例。灵感来自联邦学习，我们提出的基于云的架构使得能够在IIT设备之间分享防御功能。综合实验结果表明，我们的方法产生的DNN不仅可以抵抗比现有的攻击特定的对抗训练方法更具恶意攻击，还可以防止IIT应用来自新攻击。

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